This code covers chapter 5 of “Introduction to Data Mining” by Pang-Ning Tan, Michael Steinbach and Vipin Kumar.
This work is licensed under the Creative Commons Attribution 4.0 International License. For questions please contact Michael Hahsler.
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.2 ✓ dplyr 1.0.6
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(arules)
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
##
## Attaching package: 'arules'
## The following object is masked from 'package:dplyr':
##
## recode
## The following objects are masked from 'package:base':
##
## abbreviate, write
library(arulesViz)
data(iris)
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
Convert the data to transactions. Note that the features are numeric and need to be discretized. The conversion automatically applies frequency-based discretization with 3 classes to each numeric feature (with a warning).
iris_trans <- transactions(iris)
## Warning: Column(s) 1, 2, 3, 4 not logical or factor. Applying default
## discretization (see '? discretizeDF').
inspect(head(iris_trans))
## items transactionID
## [1] {Sepal.Length=[4.3,5.4),
## Sepal.Width=[3.2,4.4],
## Petal.Length=[1,2.63),
## Petal.Width=[0.1,0.867),
## Species=setosa} 1
## [2] {Sepal.Length=[4.3,5.4),
## Sepal.Width=[2.9,3.2),
## Petal.Length=[1,2.63),
## Petal.Width=[0.1,0.867),
## Species=setosa} 2
## [3] {Sepal.Length=[4.3,5.4),
## Sepal.Width=[3.2,4.4],
## Petal.Length=[1,2.63),
## Petal.Width=[0.1,0.867),
## Species=setosa} 3
## [4] {Sepal.Length=[4.3,5.4),
## Sepal.Width=[2.9,3.2),
## Petal.Length=[1,2.63),
## Petal.Width=[0.1,0.867),
## Species=setosa} 4
## [5] {Sepal.Length=[4.3,5.4),
## Sepal.Width=[3.2,4.4],
## Petal.Length=[1,2.63),
## Petal.Width=[0.1,0.867),
## Species=setosa} 5
## [6] {Sepal.Length=[5.4,6.3),
## Sepal.Width=[3.2,4.4],
## Petal.Length=[1,2.63),
## Petal.Width=[0.1,0.867),
## Species=setosa} 6
rules <- apriori(iris_trans, parameter = list(support = 0.1, confidence = 0.8))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 15
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[15 item(s), 150 transaction(s)] done [0.00s].
## sorting and recoding items ... [15 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [144 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
rules
## set of 144 rules
inspectDT(rules)
Plot rules as a scatter plot using an interactive html widget. To avoid overplotting, jitter is added automatically. Set jitter = 0 to disable jitter. Hovering over rules shows rule information. Note: plotly/javascript does not do well with too many points, so plot selects the top 1000 rules with a warning if more rules are supplied.
plot(rules, engine = "html")
## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
Plot rules as a matrix using an interactive html widget.
plot(rules, method = "matrix", engine = "html")
Plot rules as a graph using an interactive html widget. Note: the used javascript library does not do well with too many graph nodes, so plot selects the top 100 rules only (with a warning).
plot(rules, method = "graph", engine = "html")
## Warning: Too many rules supplied. Only plotting the best 100 rules using lift
## (change control parameter max if needed)
You can specify a rule set or a dataset. To explore rules that can be mined from iris, use: ruleExplorer(iris)
The rule explorer creates an interactive Shiny application that can be used locally or deployed on a server for sharing. A deployed version of the ruleExplorer is available here (using shinyapps.io).